Test test
Gabin, essaye de rajouter du texte en dessous de cette phrase et de supprimer “Test test” au dessus pour voir comment ça marche pour les Pull request !
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Cette ligne est une ligne ajoutée le 07/04 à 11h pour test pull. >>>>>>> ba02fe12b344d1e11cf446dc9c3c24d5e9df7e0e
je rajoute quelque chose pour voir si ca marche
library(dplyr) ## pensez à mettre les libraries ici, on s'y retrouvera plus facilement
library(ggplot2)
library(tidyr)
library(plotly)
library(kableExtra)
library(readr)
library(readxl)
library(dplyr)
library(rAmCharts)
coronavirus <- utils::read.csv("https://raw.githubusercontent.com/RamiKrispin/coronavirus-csv/master/coronavirus_dataset.csv")## URL permettant de générer la data
summary_df <- coronavirus %>% group_by(Country.Region, type) %>%
summarise(total_cases = sum(cases)) %>%
arrange(-total_cases)
summary_df %>% head(20)
## # A tibble: 20 x 3
## # Groups: Country.Region [13]
## Country.Region type total_cases
## <fct> <fct> <int>
## 1 US confirmed 461437
## 2 Spain confirmed 153222
## 3 Italy confirmed 143626
## 4 France confirmed 118781
## 5 Germany confirmed 118181
## 6 China confirmed 82883
## 7 China recovered 77679
## 8 Iran confirmed 66220
## 9 United Kingdom confirmed 65872
## 10 Germany recovered 52407
## 11 Spain recovered 52165
## 12 Turkey confirmed 42282
## 13 Iran recovered 32309
## 14 Italy recovered 28470
## 15 US recovered 25410
## 16 Belgium confirmed 24983
## 17 Switzerland confirmed 24051
## 18 France recovered 23413
## 19 Netherlands confirmed 21903
## 20 Canada confirmed 20654
coronavirus %>%
filter(date == max(date)) %>%
select(country = Country.Region, type, cases) %>%
group_by(country, type) %>%
summarise(total_cases = sum(cases)) %>%
pivot_wider(names_from = type,
values_from = total_cases) %>%
arrange(-confirmed)
## # A tibble: 184 x 4
## # Groups: country [184]
## country confirmed death recovered
## <fct> <int> <int> <int>
## 1 US 32385 1783 1851
## 2 Spain 5002 655 4144
## 3 Germany 4885 258 6107
## 4 France 4822 1341 1961
## 5 United Kingdom 4398 882 14
## 6 Italy 4204 610 1979
## 7 Turkey 4056 96 296
## 8 Brazil 1922 131 46
## 9 Iran 1634 117 2497
## 10 Belgium 1580 283 483
## # … with 174 more rows
CoronavirusFR <- filter(coronavirus, Country.Region == "France" & Province.State == "") ## j'ai mis Province.State "", comme ça on a pas l'OutreMer
ggplot(CoronavirusFR) +
aes(x = date, fill = type, colour = type, weight = cases) +
geom_bar() +
scale_fill_hue() +
scale_color_hue() +
labs(y = "Nombre de cas", title = "Coronavirus en France", caption = "Source : Rami Krispin dataset coronavirus") +
ggthemes::theme_stata() ## graphique des nouveaux cas chaque jour (non-cumulé)
CoronavirusIT <- filter(coronavirus, Country.Region == "Italy")
ggplot(CoronavirusIT) +
aes(x = date, fill = type, colour = type, weight = cases) +
geom_bar() +
scale_fill_hue() +
scale_color_hue() +
labs(y = "Nombre de cas", title = "Coronavirus en Italie", caption = "Source : Rami Krispin dataset coronavirus") +
ggthemes::theme_stata() ## graphique des nouveaux cas chaque jour (non-cumulé)
CoronavirusDE <- filter(coronavirus, Country.Region == "Germany")
ggplot(CoronavirusDE) +
aes(x = date, fill = type, colour = type, weight = cases) +
geom_bar() +
scale_fill_hue() +
scale_color_hue() +
labs(y = "Nombre de cas", title = "Coronavirus en Allemagne", caption = "Source : Rami Krispin dataset coronavirus") +
ggthemes::theme_stata() ## graphique des nouveaux cas chaque jour (non-cumulé)
CoronavirusSPA <- filter(coronavirus, Country.Region == "Spain")
ggplot(CoronavirusSPA) +
aes(x = date, fill = type, colour = type, weight = cases) +
geom_bar() +
scale_fill_hue() +
scale_color_hue() +
labs(y = "Nombre de cas", title = "Coronavirus en Espagne", caption = "Source : Rami Krispin dataset coronavirus") +
ggthemes::theme_stata() ## graphique des nouveaux cas chaque jour (non-cumulé)
CoronavirusUK <- filter(coronavirus, Country.Region == "United Kingdom")
ggplot(CoronavirusUK) +
aes(x= date, fill = type, colour = type, weight = cases) +
geom_bar() +
scale_fill_hue() +
scale_color_hue() +
labs(y = "Nombre de cas", title = "Coronavirus au Royaume-Uni", caption = "Source : Rami Krispin dataset coronavirus") +
ggthemes::theme_stata()
CombineCountries <- filter(coronavirus, Country.Region == "France" | Country.Region == "Spain" | Country.Region == "Germany" | Country.Region == "Italy")
CombineGraph <-ggplot(CombineCountries, aes(date, cases))
CombineGraph2 <- CombineGraph + geom_bar(stat = "identity", aes(fill = type)) +
facet_wrap(~ Country.Region) +
xlab("Date") +
ggtitle("Cas de coronavirus") +
theme_linedraw()
CombineGraph3 <- CombineGraph2 + theme(axis.title.y = element_blank())
CombineGraph3 <- ggplotly(CombineGraph3)
CombineGraph3 ## graphique de comparaison interactif des nouveaux cas chaque jour (non-cumulé)
DataCountries <- CombineCountries %>% group_by(Country.Region, type) %>%
summarise(total_cases = sum(cases)) %>%
arrange(Country.Region)
DataCountries2 <- spread(DataCountries, "type", "total_cases")
DataCountries2$confirmed <- as.numeric(DataCountries2$confirmed)
DataCountries2$death <- as.numeric(DataCountries2$death)
DataCountries2$recovered <- as.numeric(DataCountries2$recovered)
DataCountries2$"Ratio death/confirmed" <- DataCountries2$death/DataCountries2$confirmed*100
#DataCountries2$"Ratio recovered/confirmed" <- DataCountries2$recovered/DataCountries2$confirmed*100
library(kableExtra)
kable(DataCountries2) %>%
kable_styling("striped", full_width = F) %>%
column_spec(3, bold = T) %>%
row_spec(1, bold = T, color = "white", background = "blue") %>%
row_spec(2, bold = T, color = "white", background = "red") %>%
row_spec(3, bold = T, color = "white", background = "green") %>%
row_spec(4, bold = T, color = "white", background = "orange") ## à voir si on rajoute le ratio recovered/confirmed (pas sûr que ce soit pertinent)
| Country.Region | confirmed | death | recovered | Ratio death/confirmed |
|---|---|---|---|---|
| France | 118781 | 12228 | 23413 | 10.294576 |
| Germany | 118181 | 2607 | 52407 | 2.205938 |
| Italy | 143626 | 18279 | 28470 | 12.726804 |
| Spain | 153222 | 15447 | 52165 | 10.081450 |
summary_df2 <- spread(coronavirus, "type", "cases")
SpreadCountries <- filter(summary_df2, Country.Region == "France" & Province.State == "" | Country.Region == "Spain" | Country.Region == "Germany" | Country.Region == "Italy")
SpreadCountries1 <- SpreadCountries[,-1]
CountriesConfirmed <- SpreadCountries1 %>% group_by(Country.Region) %>% mutate(CumulConfirmes=cumsum(confirmed))
ggplot(CountriesConfirmed) +
aes(x = date, y = CumulConfirmes, colour = Country.Region) +
geom_line(size = 1L) +
scale_color_hue() +
labs(y = "Nombre de cas confirmés (cumulés)", title = "Nombre de personnes infectées par le Covid-19") +
ggthemes::theme_stata() ## graphique cumulatif des cas confirmés de Covid-19
CountriesDeath <- SpreadCountries1 %>% group_by(Country.Region) %>% mutate(CumulMort=cumsum(death))
ggplot(CountriesDeath) +
aes(x = date, y = CumulMort, colour = Country.Region) +
geom_line(size = 1L) +
scale_color_hue() +
labs(y = "Nombre de décès (cumulés)", title = "Décès liés au Covid-19") +
ggthemes::theme_stata() ## graphique cumulatif des cas de décés dû au Covid-19
LeftJoin1 <- left_join(CountriesConfirmed, CountriesDeath, by = c("Country.Region", "Lat", "Long", "date", "confirmed", "death", "recovered"))
CountriesRecovered <- SpreadCountries1 %>% group_by(Country.Region) %>% mutate(CumulSoigne=cumsum(recovered))
LeftJoin2 <- left_join(LeftJoin1, CountriesRecovered, by = c("Country.Region", "Lat", "Long", "date", "confirmed", "death", "recovered"))
LeftJoin2$confirmed <- NULL
LeftJoin2$death <- NULL
LeftJoin2$recovered <- NULL
LeftJoin2$Lat <- NULL
LeftJoin2$Long <- NULL
LeftJoin2$CumulConfirmes <- as.numeric(LeftJoin2$CumulConfirmes)
LeftJoin2$CumulMort <- as.numeric(LeftJoin2$CumulMort)
LeftJoin2$CumulSoigne <- as.numeric(LeftJoin2$CumulSoigne)
FinalCumul <- LeftJoin2 %>% gather(Total, Value, -Country.Region, -date)
ggplot(FinalCumul) +
aes(x = date, y = Value, colour = Country.Region, group = Country.Region) +
geom_line(size = 1L) +
scale_color_hue() +
labs(y = "Effectifs cumulés (par catégorie)", title = "Graphique de l'évolution du Covid-19") +
ggthemes::theme_stata() +
facet_wrap(vars(Total), scales = "free") ## à voir si on peut pas faire un graph plus parlant
CombineCumul <-ggplot(FinalCumul, aes(date, Value))
CombineCumul2 <- CombineCumul + geom_bar(stat = "identity", aes(fill = Total)) +
facet_wrap(~ Country.Region) +
xlab("Date") +
ggtitle("Effectifs cumulés par catégorie de l'évolution du Covid-19") +
theme_linedraw()
CombineCumul3 <- CombineCumul2 + theme(axis.title.y = element_blank())
CombineCumul3 <- ggplotly(CombineCumul3)
CombineCumul3 ## graph interactif sur les effectifs cumulés
CAC40 <- read_xlsx("PX1-3.xlsx")
CAC40$date <- as.Date(CAC40$date)
ggplot(CAC40) +
aes(x = date, y = cloture) +
geom_line(size = 0.78) +
scale_color_hue() +
labs(x = "Date", y = "Cours de cloture", title = "Évolution de l'indice boursier CAC 40", subtitle = "Depuis janvier 2020") +
hrbrthemes::theme_modern_rc()
SP500 <- read_xlsx("SPX.xlsx")
SP500$date <- as.Date(SP500$date) #mettre la colonne date sous le bon format
SP500 <- dplyr::rename(SP500, cloture = fermeture)
ggplot(SP500) +
aes(x = date, y = cloture) +
geom_line(size = 0.78, colour = "#0c4c8a") +
labs(x = "Date", y = "Cours de cloture", title = "Evolution du S&P 500", subtitle = "depuis janvier 2020") +
hrbrthemes::theme_modern_rc()